Overview

Dataset statistics

Number of variables21
Number of observations1437720
Missing cells0
Missing cells (%)0.0%
Duplicate rows72007
Duplicate rows (%)5.0%
Total size in memory230.3 MiB
Average record size in memory168.0 B

Variable types

Numeric14
Categorical7

Alerts

Dataset has 72007 (5.0%) duplicate rowsDuplicates
year_first_active is highly overall correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly overall correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly overall correlated with day_account_createdHigh correlation
week_of_year_first_active is highly overall correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly overall correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly overall correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly overall correlated with month_first_active and 4 other fieldsHigh correlation
month_account_created is highly overall correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly overall correlated with day_first_activeHigh correlation
week_of_year_account_created is highly overall correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly overall correlated with year_first_active and 1 other fieldsHigh correlation
language is highly imbalanced (94.3%)Imbalance
days_from_first_active_until_account_created is highly skewed (γ1 = 58.23880372)Skewed
country_destination is uniformly distributedUniform
days_from_first_active_until_account_created has 1435762 (99.9%) zerosZeros
day_of_week_first_booking has 317201 (22.1%) zerosZeros

Reproduction

Analysis started2023-01-14 19:00:03.414969
Analysis finished2023-01-14 19:02:15.777725
Duration2 minutes and 12.36 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.331894
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:15.870680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile23
Q130
median37
Q338
95-th percentile60
Maximum115
Range99
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.130432
Coefficient of variation (CV)0.32493481
Kurtosis9.1501361
Mean37.331894
Median Absolute Deviation (MAD)5
Skewness2.3533641
Sum53672811
Variance147.14738
MonotonicityNot monotonic
2023-01-14T16:02:15.981185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 398438
27.7%
30 55817
 
3.9%
31 53308
 
3.7%
32 52678
 
3.7%
29 51904
 
3.6%
28 46777
 
3.3%
34 45285
 
3.1%
33 45151
 
3.1%
27 43605
 
3.0%
35 39481
 
2.7%
Other values (89) 605276
42.1%
ValueCountFrequency (%)
16 28
 
< 0.1%
17 123
 
< 0.1%
18 7157
 
0.5%
19 13457
 
0.9%
20 3045
 
0.2%
21 11211
 
0.8%
22 17353
1.2%
23 22248
1.5%
24 28938
2.0%
25 37435
2.6%
ValueCountFrequency (%)
115 14
 
< 0.1%
113 5
 
< 0.1%
112 1
 
< 0.1%
111 3
 
< 0.1%
110 739
 
0.1%
109 44
 
< 0.1%
108 15
 
< 0.1%
107 31
 
< 0.1%
106 44
 
< 0.1%
105 12345
0.9%

days_from_first_active_until_account_created
Real number (ℝ)

SKEWED  ZEROS 

Distinct142
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28252024
Minimum0
Maximum1456
Zeros1435762
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:16.085264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.238842
Coefficient of variation (CV)43.32023
Kurtosis3984.8331
Mean0.28252024
Median Absolute Deviation (MAD)0
Skewness58.238804
Sum406185
Variance149.78925
MonotonicityNot monotonic
2023-01-14T16:02:16.191978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1435762
99.9%
5 213
 
< 0.1%
78 211
 
< 0.1%
634 113
 
< 0.1%
516 111
 
< 0.1%
1 110
 
< 0.1%
274 99
 
< 0.1%
237 86
 
< 0.1%
3 86
 
< 0.1%
40 62
 
< 0.1%
Other values (132) 867
 
0.1%
ValueCountFrequency (%)
0 1435762
99.9%
1 110
 
< 0.1%
2 14
 
< 0.1%
3 86
 
< 0.1%
4 5
 
< 0.1%
5 213
 
< 0.1%
6 61
 
< 0.1%
7 20
 
< 0.1%
9 3
 
< 0.1%
10 15
 
< 0.1%
ValueCountFrequency (%)
1456 1
 
< 0.1%
1369 3
 
< 0.1%
1361 1
 
< 0.1%
1148 1
 
< 0.1%
1036 2
 
< 0.1%
1018 1
 
< 0.1%
1011 55
< 0.1%
998 1
 
< 0.1%
995 1
 
< 0.1%
882 5
 
< 0.1%

year_first_active
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.9547
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:16.284340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.95579958
Coefficient of variation (CV)0.00047482418
Kurtosis-0.029339636
Mean2012.9547
Median Absolute Deviation (MAD)1
Skewness-0.70976666
Sum2.8940653 × 109
Variance0.91355283
MonotonicityNot monotonic
2023-01-14T16:02:16.355580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013 554217
38.5%
2014 475585
33.1%
2012 292472
20.3%
2011 98168
 
6.8%
2010 17257
 
1.2%
2009 21
 
< 0.1%
ValueCountFrequency (%)
2009 21
 
< 0.1%
2010 17257
 
1.2%
2011 98168
 
6.8%
2012 292472
20.3%
2013 554217
38.5%
2014 475585
33.1%
ValueCountFrequency (%)
2014 475585
33.1%
2013 554217
38.5%
2012 292472
20.3%
2011 98168
 
6.8%
2010 17257
 
1.2%
2009 21
 
< 0.1%

month_first_active
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9472401
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:16.430061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1113644
Coefficient of variation (CV)0.52316106
Kurtosis-0.8612958
Mean5.9472401
Median Absolute Deviation (MAD)2
Skewness0.28400373
Sum8550466
Variance9.6805885
MonotonicityNot monotonic
2023-01-14T16:02:16.499068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 198716
13.8%
6 191474
13.3%
4 153337
10.7%
3 138108
9.6%
2 110136
7.7%
8 107534
7.5%
9 105078
7.3%
1 104123
7.2%
7 90799
6.3%
10 82198
5.7%
Other values (2) 156217
10.9%
ValueCountFrequency (%)
1 104123
7.2%
2 110136
7.7%
3 138108
9.6%
4 153337
10.7%
5 198716
13.8%
6 191474
13.3%
7 90799
6.3%
8 107534
7.5%
9 105078
7.3%
10 82198
5.7%
ValueCountFrequency (%)
12 74041
 
5.1%
11 82176
5.7%
10 82198
5.7%
9 105078
7.3%
8 107534
7.5%
7 90799
6.3%
6 191474
13.3%
5 198716
13.8%
4 153337
10.7%
3 138108
9.6%

day_first_active
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.85175
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:16.580222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7522146
Coefficient of variation (CV)0.55212924
Kurtosis-1.1972518
Mean15.85175
Median Absolute Deviation (MAD)8
Skewness-0.001219868
Sum22790378
Variance76.601261
MonotonicityNot monotonic
2023-01-14T16:02:16.665506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
28 52344
 
3.6%
3 52043
 
3.6%
13 50837
 
3.5%
17 50529
 
3.5%
24 50104
 
3.5%
5 49737
 
3.5%
16 49590
 
3.4%
10 48981
 
3.4%
7 48409
 
3.4%
21 48318
 
3.4%
Other values (21) 936828
65.2%
ValueCountFrequency (%)
1 39336
2.7%
2 43485
3.0%
3 52043
3.6%
4 43948
3.1%
5 49737
3.5%
6 46141
3.2%
7 48409
3.4%
8 48114
3.3%
9 47372
3.3%
10 48981
3.4%
ValueCountFrequency (%)
31 24672
1.7%
30 47956
3.3%
29 41448
2.9%
28 52344
3.6%
27 45658
3.2%
26 46772
3.3%
25 47655
3.3%
24 50104
3.5%
23 48053
3.3%
22 47286
3.3%

week_of_year_first_active
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.087601
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:16.767609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.474984
Coefficient of variation (CV)0.55941579
Kurtosis-0.85159792
Mean24.087601
Median Absolute Deviation (MAD)10
Skewness0.2879042
Sum34631225
Variance181.5752
MonotonicityNot monotonic
2023-01-14T16:02:16.874639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 47852
 
3.3%
26 47062
 
3.3%
23 46920
 
3.3%
21 44769
 
3.1%
22 43371
 
3.0%
20 43172
 
3.0%
25 43055
 
3.0%
24 42323
 
2.9%
18 42106
 
2.9%
17 37707
 
2.6%
Other values (43) 999383
69.5%
ValueCountFrequency (%)
1 20432
1.4%
2 18098
1.3%
3 25373
1.8%
4 27393
1.9%
5 24696
1.7%
6 26162
1.8%
7 26461
1.8%
8 30085
2.1%
9 31067
2.2%
10 29845
2.1%
ValueCountFrequency (%)
53 8
 
< 0.1%
52 16147
1.1%
51 15922
1.1%
50 16975
1.2%
49 17955
1.2%
48 17198
1.2%
47 19008
1.3%
46 20443
1.4%
45 19279
1.3%
44 18217
1.3%

year_first_booking
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.2113
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:16.959466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12013
median2013
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0798515
Coefficient of variation (CV)0.00053638258
Kurtosis-0.13289893
Mean2013.2113
Median Absolute Deviation (MAD)1
Skewness-0.4130753
Sum2.8944341 × 109
Variance1.1660792
MonotonicityNot monotonic
2023-01-14T16:02:17.029888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2014 476121
33.1%
2013 474021
33.0%
2012 252598
17.6%
2015 141152
 
9.8%
2011 79421
 
5.5%
2010 14407
 
1.0%
ValueCountFrequency (%)
2010 14407
 
1.0%
2011 79421
 
5.5%
2012 252598
17.6%
2013 474021
33.0%
2014 476121
33.1%
2015 141152
 
9.8%
ValueCountFrequency (%)
2015 141152
 
9.8%
2014 476121
33.1%
2013 474021
33.0%
2012 252598
17.6%
2011 79421
 
5.5%
2010 14407
 
1.0%

month_first_booking
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0412549
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:17.101965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8998032
Coefficient of variation (CV)0.48000015
Kurtosis-0.63677055
Mean6.0412549
Median Absolute Deviation (MAD)2
Skewness0.24521177
Sum8685633
Variance8.4088588
MonotonicityNot monotonic
2023-01-14T16:02:17.172679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 295664
20.6%
5 180036
12.5%
4 134076
9.3%
3 121326
8.4%
7 108152
 
7.5%
8 107974
 
7.5%
2 98857
 
6.9%
9 98175
 
6.8%
10 81496
 
5.7%
1 79892
 
5.6%
Other values (2) 132072
9.2%
ValueCountFrequency (%)
1 79892
 
5.6%
2 98857
 
6.9%
3 121326
8.4%
4 134076
9.3%
5 180036
12.5%
6 295664
20.6%
7 108152
 
7.5%
8 107974
 
7.5%
9 98175
 
6.8%
10 81496
 
5.7%
ValueCountFrequency (%)
12 61398
 
4.3%
11 70674
 
4.9%
10 81496
 
5.7%
9 98175
 
6.8%
8 107974
 
7.5%
7 108152
 
7.5%
6 295664
20.6%
5 180036
12.5%
4 134076
9.3%
3 121326
8.4%

day_first_booking
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.753026
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:17.258341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median17
Q325
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.1435575
Coefficient of variation (CV)0.54578542
Kurtosis-1.2772498
Mean16.753026
Median Absolute Deviation (MAD)8
Skewness-0.094355362
Sum24086161
Variance83.604644
MonotonicityNot monotonic
2023-01-14T16:02:17.346591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
29 158226
 
11.0%
17 47259
 
3.3%
5 47042
 
3.3%
21 46657
 
3.2%
25 46571
 
3.2%
15 46527
 
3.2%
3 45785
 
3.2%
11 44880
 
3.1%
10 44645
 
3.1%
23 44504
 
3.1%
Other values (21) 865624
60.2%
ValueCountFrequency (%)
1 43350
3.0%
2 39891
2.8%
3 45785
3.2%
4 41598
2.9%
5 47042
3.3%
6 42587
3.0%
7 43070
3.0%
8 42887
3.0%
9 44427
3.1%
10 44645
3.1%
ValueCountFrequency (%)
31 21724
 
1.5%
30 39930
 
2.8%
29 158226
11.0%
28 41733
 
2.9%
27 40780
 
2.8%
26 42594
 
3.0%
25 46571
 
3.2%
24 44380
 
3.1%
23 44504
 
3.1%
22 39827
 
2.8%

day_of_week_first_booking
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5779199
Minimum0
Maximum6
Zeros317201
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:17.427807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0274874
Coefficient of variation (CV)0.78648192
Kurtosis-1.2095423
Mean2.5779199
Median Absolute Deviation (MAD)2
Skewness0.2412361
Sum3706327
Variance4.1107051
MonotonicityNot monotonic
2023-01-14T16:02:17.493480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 317201
22.1%
1 212607
14.8%
2 206274
14.3%
3 204736
14.2%
4 177391
12.3%
6 159845
11.1%
5 159666
11.1%
ValueCountFrequency (%)
0 317201
22.1%
1 212607
14.8%
2 206274
14.3%
3 204736
14.2%
4 177391
12.3%
5 159666
11.1%
6 159845
11.1%
ValueCountFrequency (%)
6 159845
11.1%
5 159666
11.1%
4 177391
12.3%
3 204736
14.2%
2 206274
14.3%
1 212607
14.8%
0 317201
22.1%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.719971
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:17.585086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q115
median25
Q333
95-th percentile47
Maximum53
Range52
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.585084
Coefficient of variation (CV)0.50910593
Kurtosis-0.65891785
Mean24.719971
Median Absolute Deviation (MAD)9
Skewness0.19552374
Sum35540396
Variance158.38433
MonotonicityNot monotonic
2023-01-14T16:02:17.694226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 150045
 
10.4%
24 42765
 
3.0%
21 42283
 
2.9%
26 41318
 
2.9%
23 41119
 
2.9%
19 40926
 
2.8%
25 40717
 
2.8%
22 40482
 
2.8%
20 39530
 
2.7%
18 35743
 
2.5%
Other values (43) 922792
64.2%
ValueCountFrequency (%)
1 13456
0.9%
2 15685
1.1%
3 21093
1.5%
4 20161
1.4%
5 20696
1.4%
6 22455
1.6%
7 24841
1.7%
8 27228
1.9%
9 26103
1.8%
10 27604
1.9%
ValueCountFrequency (%)
53 3
 
< 0.1%
52 12753
0.9%
51 13457
0.9%
50 15296
1.1%
49 14768
1.0%
48 14710
1.0%
47 14471
1.0%
46 17658
1.2%
45 17802
1.2%
44 17461
1.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
2013
554386 
2014
475706 
2012
292437 
2011
98098 
2010
 
17093

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5750880
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2011
3rd row2010
4th row2011
5th row2010

Common Values

ValueCountFrequency (%)
2013 554386
38.6%
2014 475706
33.1%
2012 292437
20.3%
2011 98098
 
6.8%
2010 17093
 
1.2%

Length

2023-01-14T16:02:17.791666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-14T16:02:17.880543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 554386
38.6%
2014 475706
33.1%
2012 292437
20.3%
2011 98098
 
6.8%
2010 17093
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2 1730157
30.1%
1 1535818
26.7%
0 1454813
25.3%
3 554386
 
9.6%
4 475706
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5750880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1730157
30.1%
1 1535818
26.7%
0 1454813
25.3%
3 554386
 
9.6%
4 475706
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5750880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1730157
30.1%
1 1535818
26.7%
0 1454813
25.3%
3 554386
 
9.6%
4 475706
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5750880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1730157
30.1%
1 1535818
26.7%
0 1454813
25.3%
3 554386
 
9.6%
4 475706
 
8.3%

month_account_created
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9491876
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:17.958520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1124201
Coefficient of variation (CV)0.52316724
Kurtosis-0.86267944
Mean5.9491876
Median Absolute Deviation (MAD)2
Skewness0.28326515
Sum8553266
Variance9.6871586
MonotonicityNot monotonic
2023-01-14T16:02:18.027589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 198680
13.8%
6 191436
13.3%
4 153215
10.7%
3 138003
9.6%
2 110020
7.7%
8 107499
7.5%
9 104944
7.3%
1 104184
7.2%
7 90808
6.3%
11 82589
5.7%
Other values (2) 156342
10.9%
ValueCountFrequency (%)
1 104184
7.2%
2 110020
7.7%
3 138003
9.6%
4 153215
10.7%
5 198680
13.8%
6 191436
13.3%
7 90808
6.3%
8 107499
7.5%
9 104944
7.3%
10 82285
5.7%
ValueCountFrequency (%)
12 74057
 
5.2%
11 82589
5.7%
10 82285
5.7%
9 104944
7.3%
8 107499
7.5%
7 90808
6.3%
6 191436
13.3%
5 198680
13.8%
4 153215
10.7%
3 138003
9.6%

day_account_created
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.853177
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:18.108940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7532198
Coefficient of variation (CV)0.55214296
Kurtosis-1.197346
Mean15.853177
Median Absolute Deviation (MAD)8
Skewness-0.001484195
Sum22792429
Variance76.618857
MonotonicityNot monotonic
2023-01-14T16:02:18.195595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
28 52491
 
3.7%
3 52066
 
3.6%
13 50778
 
3.5%
17 50477
 
3.5%
24 50055
 
3.5%
16 49710
 
3.5%
5 49693
 
3.5%
10 49084
 
3.4%
21 48398
 
3.4%
7 48379
 
3.4%
Other values (21) 936589
65.1%
ValueCountFrequency (%)
1 39368
2.7%
2 43502
3.0%
3 52066
3.6%
4 43942
3.1%
5 49693
3.5%
6 46134
3.2%
7 48379
3.4%
8 48162
3.3%
9 47297
3.3%
10 49084
3.4%
ValueCountFrequency (%)
31 24636
1.7%
30 47959
3.3%
29 41590
2.9%
28 52491
3.7%
27 45656
3.2%
26 46763
3.3%
25 47541
3.3%
24 50055
3.5%
23 47893
3.3%
22 47367
3.3%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.096319
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 MiB
2023-01-14T16:02:18.297463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.480468
Coefficient of variation (CV)0.55944097
Kurtosis-0.85296078
Mean24.096319
Median Absolute Deviation (MAD)10
Skewness0.28736465
Sum34643760
Variance181.72302
MonotonicityNot monotonic
2023-01-14T16:02:18.408954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 47848
 
3.3%
26 47059
 
3.3%
23 46898
 
3.3%
21 44767
 
3.1%
22 43375
 
3.0%
20 43128
 
3.0%
25 43058
 
3.0%
24 42370
 
2.9%
18 42119
 
2.9%
17 37765
 
2.6%
Other values (43) 999333
69.5%
ValueCountFrequency (%)
1 20435
1.4%
2 18100
1.3%
3 25373
1.8%
4 27393
1.9%
5 24749
1.7%
6 26162
1.8%
7 26454
1.8%
8 29975
2.1%
9 31071
2.2%
10 29844
2.1%
ValueCountFrequency (%)
53 8
 
< 0.1%
52 16147
1.1%
51 15921
1.1%
50 16973
1.2%
49 17975
1.3%
48 17407
1.2%
47 19210
1.3%
46 20437
1.4%
45 19288
1.3%
44 18228
1.3%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
FEMALE
506800 
-unknown-
474989 
MALE
452413 
OTHER
 
3518

Length

Max length9
Median length6
Mean length6.3593349
Min length4

Characters and Unicode

Total characters9142943
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th row-unknown-

Common Values

ValueCountFrequency (%)
FEMALE 506800
35.3%
-unknown- 474989
33.0%
MALE 452413
31.5%
OTHER 3518
 
0.2%

Length

2023-01-14T16:02:18.503226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-14T16:02:18.590693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
female 506800
35.3%
unknown 474989
33.0%
male 452413
31.5%
other 3518
 
0.2%

Most occurring characters

ValueCountFrequency (%)
E 1469531
16.1%
n 1424967
15.6%
M 959213
10.5%
A 959213
10.5%
L 959213
10.5%
- 949978
10.4%
F 506800
 
5.5%
u 474989
 
5.2%
k 474989
 
5.2%
o 474989
 
5.2%
Other values (5) 489061
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4868042
53.2%
Lowercase Letter 3324923
36.4%
Dash Punctuation 949978
 
10.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1469531
30.2%
M 959213
19.7%
A 959213
19.7%
L 959213
19.7%
F 506800
 
10.4%
O 3518
 
0.1%
T 3518
 
0.1%
H 3518
 
0.1%
R 3518
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
n 1424967
42.9%
u 474989
 
14.3%
k 474989
 
14.3%
o 474989
 
14.3%
w 474989
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 949978
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8192965
89.6%
Common 949978
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1469531
17.9%
n 1424967
17.4%
M 959213
11.7%
A 959213
11.7%
L 959213
11.7%
F 506800
 
6.2%
u 474989
 
5.8%
k 474989
 
5.8%
o 474989
 
5.8%
w 474989
 
5.8%
Other values (4) 14072
 
0.2%
Common
ValueCountFrequency (%)
- 949978
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9142943
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1469531
16.1%
n 1424967
15.6%
M 959213
10.5%
A 959213
10.5%
L 959213
10.5%
- 949978
10.4%
F 506800
 
5.5%
u 474989
 
5.2%
k 474989
 
5.2%
o 474989
 
5.2%
Other values (5) 489061
 
5.3%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
basic
1047240 
facebook
388720 
google
 
1760

Length

Max length8
Median length5
Mean length5.8123418
Min length5

Characters and Unicode

Total characters8356520
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowbasic
4th rowfacebook
5th rowbasic

Common Values

ValueCountFrequency (%)
basic 1047240
72.8%
facebook 388720
 
27.0%
google 1760
 
0.1%

Length

2023-01-14T16:02:19.065451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-14T16:02:19.160032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
basic 1047240
72.8%
facebook 388720
 
27.0%
google 1760
 
0.1%

Most occurring characters

ValueCountFrequency (%)
b 1435960
17.2%
a 1435960
17.2%
c 1435960
17.2%
s 1047240
12.5%
i 1047240
12.5%
o 780960
9.3%
e 390480
 
4.7%
f 388720
 
4.7%
k 388720
 
4.7%
g 3520
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8356520
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 1435960
17.2%
a 1435960
17.2%
c 1435960
17.2%
s 1047240
12.5%
i 1047240
12.5%
o 780960
9.3%
e 390480
 
4.7%
f 388720
 
4.7%
k 388720
 
4.7%
g 3520
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8356520
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 1435960
17.2%
a 1435960
17.2%
c 1435960
17.2%
s 1047240
12.5%
i 1047240
12.5%
o 780960
9.3%
e 390480
 
4.7%
f 388720
 
4.7%
k 388720
 
4.7%
g 3520
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8356520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 1435960
17.2%
a 1435960
17.2%
c 1435960
17.2%
s 1047240
12.5%
i 1047240
12.5%
o 780960
9.3%
e 390480
 
4.7%
f 388720
 
4.7%
k 388720
 
4.7%
g 3520
 
< 0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
en
1399438 
fr
 
8519
de
 
6563
es
 
5623
zh
 
4567
Other values (20)
 
13010

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2875440
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en 1399438
97.3%
fr 8519
 
0.6%
de 6563
 
0.5%
es 5623
 
0.4%
zh 4567
 
0.3%
ko 2913
 
0.2%
it 2622
 
0.2%
ru 1619
 
0.1%
pt 1205
 
0.1%
nl 1183
 
0.1%
Other values (15) 3468
 
0.2%

Length

2023-01-14T16:02:19.234414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 1399438
97.3%
fr 8519
 
0.6%
de 6563
 
0.5%
es 5623
 
0.4%
zh 4567
 
0.3%
ko 2913
 
0.2%
it 2622
 
0.2%
ru 1619
 
0.1%
pt 1205
 
0.1%
nl 1183
 
0.1%
Other values (15) 3468
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 1411793
49.1%
n 1400796
48.7%
r 10329
 
0.4%
f 8570
 
0.3%
d 6843
 
0.2%
s 6404
 
0.2%
h 4634
 
0.2%
z 4567
 
0.2%
t 4053
 
0.1%
o 3088
 
0.1%
Other values (9) 14363
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2875440
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1411793
49.1%
n 1400796
48.7%
r 10329
 
0.4%
f 8570
 
0.3%
d 6843
 
0.2%
s 6404
 
0.2%
h 4634
 
0.2%
z 4567
 
0.2%
t 4053
 
0.1%
o 3088
 
0.1%
Other values (9) 14363
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2875440
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1411793
49.1%
n 1400796
48.7%
r 10329
 
0.4%
f 8570
 
0.3%
d 6843
 
0.2%
s 6404
 
0.2%
h 4634
 
0.2%
z 4567
 
0.2%
t 4053
 
0.1%
o 3088
 
0.1%
Other values (9) 14363
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2875440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1411793
49.1%
n 1400796
48.7%
r 10329
 
0.4%
f 8570
 
0.3%
d 6843
 
0.2%
s 6404
 
0.2%
h 4634
 
0.2%
z 4567
 
0.2%
t 4053
 
0.1%
o 3088
 
0.1%
Other values (9) 14363
 
0.5%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
direct
956628 
sem-brand
191362 
sem-non-brand
132713 
seo
 
60680
other
 
40030
Other values (3)
 
56307

Length

Max length13
Median length6
Mean length6.8517806
Min length3

Characters and Unicode

Total characters9850942
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct 956628
66.5%
sem-brand 191362
 
13.3%
sem-non-brand 132713
 
9.2%
seo 60680
 
4.2%
other 40030
 
2.8%
api 35501
 
2.5%
content 13478
 
0.9%
remarketing 7328
 
0.5%

Length

2023-01-14T16:02:19.315778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-14T16:02:19.416038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
direct 956628
66.5%
sem-brand 191362
 
13.3%
sem-non-brand 132713
 
9.2%
seo 60680
 
4.2%
other 40030
 
2.8%
api 35501
 
2.5%
content 13478
 
0.9%
remarketing 7328
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 1409547
14.3%
r 1335389
13.6%
d 1280703
13.0%
t 1030942
10.5%
i 999457
10.1%
c 970106
9.8%
n 623785
6.3%
- 456788
 
4.6%
s 384755
 
3.9%
a 366904
 
3.7%
Other values (7) 992566
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9394154
95.4%
Dash Punctuation 456788
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1409547
15.0%
r 1335389
14.2%
d 1280703
13.6%
t 1030942
11.0%
i 999457
10.6%
c 970106
10.3%
n 623785
6.6%
s 384755
 
4.1%
a 366904
 
3.9%
m 331403
 
3.5%
Other values (6) 661163
7.0%
Dash Punctuation
ValueCountFrequency (%)
- 456788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9394154
95.4%
Common 456788
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1409547
15.0%
r 1335389
14.2%
d 1280703
13.6%
t 1030942
11.0%
i 999457
10.6%
c 970106
10.3%
n 623785
6.6%
s 384755
 
4.1%
a 366904
 
3.9%
m 331403
 
3.5%
Other values (6) 661163
7.0%
Common
ValueCountFrequency (%)
- 456788
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9850942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1409547
14.3%
r 1335389
13.6%
d 1280703
13.0%
t 1030942
10.5%
i 999457
10.1%
c 970106
9.8%
n 623785
6.3%
- 456788
 
4.6%
s 384755
 
3.9%
a 366904
 
3.7%
Other values (7) 992566
10.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
untracked
782833 
linked
313357 
omg
297852 
tracked-other
 
29534
product
 
13330
Other values (2)
 
814

Length

Max length13
Median length9
Mean length7.1667453
Min length3

Characters and Unicode

Total characters10303773
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked 782833
54.4%
linked 313357
21.8%
omg 297852
 
20.7%
tracked-other 29534
 
2.1%
product 13330
 
0.9%
marketing 492
 
< 0.1%
local ops 322
 
< 0.1%

Length

2023-01-14T16:02:19.508757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-14T16:02:19.598708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
untracked 782833
54.4%
linked 313357
21.8%
omg 297852
 
20.7%
tracked-other 29534
 
2.1%
product 13330
 
0.9%
marketing 492
 
< 0.1%
local 322
 
< 0.1%
ops 322
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 1155750
11.2%
d 1139054
11.1%
k 1126216
10.9%
n 1096682
10.6%
t 855723
8.3%
r 855723
8.3%
c 826019
8.0%
a 813181
7.9%
u 796163
7.7%
o 341360
 
3.3%
Other values (9) 1297902
12.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10273917
99.7%
Dash Punctuation 29534
 
0.3%
Space Separator 322
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1155750
11.2%
d 1139054
11.1%
k 1126216
11.0%
n 1096682
10.7%
t 855723
8.3%
r 855723
8.3%
c 826019
8.0%
a 813181
7.9%
u 796163
7.7%
o 341360
 
3.3%
Other values (7) 1268046
12.3%
Dash Punctuation
ValueCountFrequency (%)
- 29534
100.0%
Space Separator
ValueCountFrequency (%)
322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10273917
99.7%
Common 29856
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1155750
11.2%
d 1139054
11.1%
k 1126216
11.0%
n 1096682
10.7%
t 855723
8.3%
r 855723
8.3%
c 826019
8.0%
a 813181
7.9%
u 796163
7.7%
o 341360
 
3.3%
Other values (7) 1268046
12.3%
Common
ValueCountFrequency (%)
- 29534
98.9%
322
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10303773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1155750
11.2%
d 1139054
11.1%
k 1126216
10.9%
n 1096682
10.6%
t 855723
8.3%
r 855723
8.3%
c 826019
8.0%
a 813181
7.9%
u 796163
7.7%
o 341360
 
3.3%
Other values (9) 1297902
12.6%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 MiB
NDF
119810 
US
119810 
other
119810 
CA
119810 
FR
119810 
Other values (7)
838670 

Length

Max length5
Median length2
Mean length2.3333333
Min length2

Characters and Unicode

Total characters3354680
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowUS
4th rowother
5th rowUS

Common Values

ValueCountFrequency (%)
NDF 119810
8.3%
US 119810
8.3%
other 119810
8.3%
CA 119810
8.3%
FR 119810
8.3%
ES 119810
8.3%
GB 119810
8.3%
IT 119810
8.3%
PT 119810
8.3%
NL 119810
8.3%
Other values (2) 239620
16.7%

Length

2023-01-14T16:02:19.690811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf 119810
8.3%
us 119810
8.3%
other 119810
8.3%
ca 119810
8.3%
fr 119810
8.3%
es 119810
8.3%
gb 119810
8.3%
it 119810
8.3%
pt 119810
8.3%
nl 119810
8.3%
Other values (2) 239620
16.7%

Most occurring characters

ValueCountFrequency (%)
N 239620
 
7.1%
E 239620
 
7.1%
F 239620
 
7.1%
U 239620
 
7.1%
S 239620
 
7.1%
T 239620
 
7.1%
D 239620
 
7.1%
A 239620
 
7.1%
P 119810
 
3.6%
I 119810
 
3.6%
Other values (10) 1198100
35.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2755630
82.1%
Lowercase Letter 599050
 
17.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 239620
 
8.7%
E 239620
 
8.7%
F 239620
 
8.7%
U 239620
 
8.7%
S 239620
 
8.7%
T 239620
 
8.7%
D 239620
 
8.7%
A 239620
 
8.7%
P 119810
 
4.3%
I 119810
 
4.3%
Other values (5) 599050
21.7%
Lowercase Letter
ValueCountFrequency (%)
r 119810
20.0%
e 119810
20.0%
h 119810
20.0%
t 119810
20.0%
o 119810
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3354680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 239620
 
7.1%
E 239620
 
7.1%
F 239620
 
7.1%
U 239620
 
7.1%
S 239620
 
7.1%
T 239620
 
7.1%
D 239620
 
7.1%
A 239620
 
7.1%
P 119810
 
3.6%
I 119810
 
3.6%
Other values (10) 1198100
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3354680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 239620
 
7.1%
E 239620
 
7.1%
F 239620
 
7.1%
U 239620
 
7.1%
S 239620
 
7.1%
T 239620
 
7.1%
D 239620
 
7.1%
A 239620
 
7.1%
P 119810
 
3.6%
I 119810
 
3.6%
Other values (10) 1198100
35.7%

Interactions

2023-01-14T16:02:03.271585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:57.479325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:02.324701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:07.034333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:12.624886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:17.536709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:22.479152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:27.454332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:32.682062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:37.710763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:42.793846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:47.982532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:52.977029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:58.265881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:03.629547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:57.834789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:02.655948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:07.384264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:12.997490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:17.886539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:22.829970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:27.805807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:33.034350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:38.065790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:43.148265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:48.333900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:53.326116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:58.620750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:03.991094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:58.182490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:02.998782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:07.734236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:13.352501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:18.250934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:23.190479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:28.168575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:33.401451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:38.430638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:43.518083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:48.696565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:53.686357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:58.989493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:04.370410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:58.530931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:03.333519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:08.087776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:13.703424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:18.604839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:23.543698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:28.524166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:33.754061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:38.788763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:43.877670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:49.049983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:54.037207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:59.346455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:04.734822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:58.878742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:03.664158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:08.446967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:14.051369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:18.949416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:23.893448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:28.876692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:34.112243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:39.145487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:44.272391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:49.404712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:54.390382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:59.699651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:05.095785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:59.225748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:03.997906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:08.796627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:14.399712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:19.309455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:24.244955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:29.235212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:34.476195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:39.505522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:44.700356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:49.765792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:54.755751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:00.055684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:05.447344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:59.563635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:04.332476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:09.141992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:14.753079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:19.659679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:24.611331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:29.584457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:34.829689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:39.861500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:45.072338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:50.119042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:55.109028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:00.407103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:05.802971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:00:59.910921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:04.667837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:09.495313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:15.102917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:20.013078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:24.969476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:29.945169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:35.181797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:40.220804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:45.432556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:50.477437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:55.459031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:00.763097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:06.157285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:00.261402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:05.001921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:09.844813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:15.449529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:20.367115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:25.321130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:30.296923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:35.546326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:40.574263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:45.799747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:50.830083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:56.117121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:01.121537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:06.516988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:00.612054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:05.341566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:10.199294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:15.797128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:20.722091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:25.676715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:30.649342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:35.902623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:40.940144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:46.162252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:51.185202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:56.476350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:01.480150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:06.875455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:00.962607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:05.676612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:10.550408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:16.145422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:21.075914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:26.032825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:31.003732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:36.258371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:41.299949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:46.529174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:51.537306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:56.833385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:01.836714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:07.229466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:01.307176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:06.010248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:10.899688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:16.491066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:21.423969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:26.386835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:31.358310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:36.615907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:41.652375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:46.889369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:51.896758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:57.187393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:02.194130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:07.581910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:01.651789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:06.341269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:11.249549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:16.839899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:21.777320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:26.742285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:31.958470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:36.973210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:42.022465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:47.255349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:52.254266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:57.548320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:02.548254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:07.926770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:02.001215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:06.675239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:11.598694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:17.187041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:22.127838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:27.099207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:32.320650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:37.349185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:42.431125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:47.622411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:52.620039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:01:57.907561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-14T16:02:02.910827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-14T16:02:19.783176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
agedays_from_first_active_until_account_createdyear_first_activemonth_first_activeday_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingmonth_account_createdday_account_createdweek_of_year_account_createdyear_account_createdgendersignup_methodlanguageaffiliate_channelfirst_affiliate_trackedcountry_destination
age1.0000.007-0.053-0.011-0.002-0.010-0.033-0.0160.0180.010-0.010-0.010-0.001-0.0090.0570.2530.1750.0290.0700.0760.062
days_from_first_active_until_account_created0.0071.000-0.0390.0090.0070.009-0.0170.012-0.006-0.0040.0100.0240.0120.0250.0160.0190.0150.0000.0230.0070.018
year_first_active-0.053-0.0391.000-0.4390.005-0.4270.813-0.2950.042-0.023-0.283-0.4400.005-0.4270.9980.0890.0760.0280.1130.0630.057
month_first_active-0.0110.009-0.4391.000-0.0200.980-0.2670.661-0.005-0.0070.6500.999-0.0190.9790.2770.0470.0430.0330.0600.0440.053
day_first_active-0.0020.0070.005-0.0201.0000.0570.016-0.0030.3450.0110.026-0.0190.9990.0580.0350.0290.0290.0340.0210.0320.030
week_of_year_first_active-0.0100.009-0.4270.9800.0571.000-0.2630.6620.027-0.0100.6580.9790.0580.9990.2680.0480.0510.0340.0580.0490.055
year_first_booking-0.033-0.0170.813-0.2670.016-0.2631.000-0.3090.215-0.205-0.264-0.2670.016-0.2630.8600.0970.0690.0330.1070.0590.411
month_first_booking-0.0160.012-0.2950.661-0.0030.662-0.3091.000-0.012-0.0250.9800.661-0.0030.6620.1860.0680.0370.0330.0450.0420.209
day_first_booking0.018-0.0060.042-0.0050.3450.0270.215-0.0121.000-0.1640.085-0.0060.3450.0270.0380.0630.0390.0380.0290.0360.238
day_of_week_first_booking0.010-0.004-0.023-0.0070.011-0.010-0.205-0.025-0.1641.000-0.062-0.0070.011-0.0100.0340.0490.0310.0360.0270.0290.234
week_of_year_first_booking-0.0100.010-0.2830.6500.0260.658-0.2640.9800.085-0.0621.0000.6500.0260.6580.1830.0630.0390.0380.0420.0460.189
month_account_created-0.0100.024-0.4400.999-0.0190.979-0.2670.661-0.006-0.0070.6501.000-0.0190.9800.2780.0470.0440.0330.0600.0440.053
day_account_created-0.0010.0120.005-0.0190.9990.0580.016-0.0030.3450.0110.026-0.0191.0000.0580.0350.0300.0300.0340.0210.0320.030
week_of_year_account_created-0.0090.025-0.4270.9790.0580.999-0.2630.6620.027-0.0100.6580.9800.0581.0000.2680.0480.0510.0340.0590.0490.055
year_account_created0.0570.0160.9980.2770.0350.2680.8600.1860.0380.0340.1830.2780.0350.2681.0000.0890.0770.0310.1260.0700.063
gender0.2530.0190.0890.0470.0290.0480.0970.0680.0630.0490.0630.0470.0300.0480.0891.0000.2830.0370.0510.0430.095
signup_method0.1750.0150.0760.0430.0290.0510.0690.0370.0390.0310.0390.0440.0300.0510.0770.2831.0000.0340.1080.0410.044
language0.0290.0000.0280.0330.0340.0340.0330.0330.0380.0360.0380.0330.0340.0340.0310.0370.0341.0000.0450.0930.051
affiliate_channel0.0700.0230.1130.0600.0210.0580.1070.0450.0290.0270.0420.0600.0210.0590.1260.0510.1080.0451.0000.3250.050
first_affiliate_tracked0.0760.0070.0630.0440.0320.0490.0590.0420.0360.0290.0460.0440.0320.0490.0700.0430.0410.0930.3251.0000.050
country_destination0.0620.0180.0570.0530.0300.0550.4110.2090.2380.2340.1890.0530.0300.0550.0630.0950.0440.0510.0500.0501.000

Missing values

2023-01-14T16:02:08.868851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-14T16:02:11.373198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agedays_from_first_active_until_account_createdyear_first_activemonth_first_activeday_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelfirst_affiliate_trackedcountry_destination
0374662009319122015629027201062826-unknown-facebookendirectuntrackedNDF
1387322009523212015629027201152521MALEfacebookenseountrackedNDF
25647620096924201082031201092839FEMALEbasicendirectuntrackedUS
3427652009103144201298536201112549FEMALEfacebookendirectuntrackedother
441280200912850201021837201091437-unknown-basicendirectuntrackedUS
53702010115320101255320101153-unknown-basicenotheromgUS
6460201012532010151120101253FEMALEbasicenotheruntrackedUS
74702010135320101132220101353FEMALEbasicendirectomgUS
8500201014120107293302010141FEMALEbasicenotheruntrackedUS
94602010141201014012010141-unknown-basicenotheromgUS
agedays_from_first_active_until_account_createdyear_first_activemonth_first_activeday_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelfirst_affiliate_trackedcountry_destination
14377103702014417162014426517201441716FEMALEbasicendirectuntrackedother
14377116402012121482012126349201212148MALEbasicendirectuntrackedother
14377123702012126492012127449201212649-unknown-basicendirectuntrackedother
1437713710201332212201348015201332212FEMALEbasicendirectomgother
143771410502013310102013311011201331010-unknown-basicendirectuntrackedother
14377153702014672320146752320146723-unknown-basicendirectuntrackedother
143771637020133410201331801220133410-unknown-basicensem-non-brandlinkedother
1437717310201312295220141115442013122952MALEfacebookendirectuntrackedother
14377183702012725302012726330201272530MALEbasicensem-non-brandlinkedother
14377196102014112220147812820141122MALEbasicenseolinkedother

Duplicate rows

Most frequently occurring

agedays_from_first_active_until_account_createdyear_first_activemonth_first_activeday_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelfirst_affiliate_trackedcountry_destination# duplicates
529983702014529222014529322201452922-unknown-basicendirectuntrackedPT1119
454073702013107412013107041201310741-unknown-basicendirectuntrackedPT630
7075969020145719201452232120145719MALEbasicensem-brandomgPT621
319593602013713282013713528201371328-unknown-basicendirectuntrackedPT606
46840370201311304820131231492013113048-unknown-basicendirectlinkedPT603
64819020146223201461342420146223FEMALEbasicendirectomgPT602
542193702014628262014628526201462826-unknown-basicendirectlinkedPT602
610414402012912372012913337201291237FEMALEbasicendirectuntrackedPT602
2183232020111219512012431142011121951FEMALEfacebookendirectuntrackedPT601
16669300201293039201322468201293039FEMALEbasicendirectproductPT600